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  • Mobilitäts- und Verkehrsforschung  (4)
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  • Mobilitäts- und Verkehrsforschung  (4)
  • 1
    Online-Ressource
    Online-Ressource
    3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data
    SAGE Publications ; 2018
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2672, No. 45 ( 2018-12), p. 14-22
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2672, No. 45 ( 2018-12), p. 14-22
    Kurzfassung: High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.
    Materialart: Online-Ressource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198118775839
    Sprache: Englisch
    Verlag: SAGE Publications
    Publikationsdatum: 2018
    ZDB Id: 2403378-9
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Online-Ressource
  • 2
    Online-Ressource
    Online-Ressource
    Vehicle Detection and Tracking in Complex Traffic Circumstances with Roadside LiDAR
    SAGE Publications ; 2019
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2673, No. 9 ( 2019-09), p. 62-71
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2673, No. 9 ( 2019-09), p. 62-71
    Kurzfassung: The problem of traffic safety has become increasingly prominent owing to the increase in the number of cars. Traffic accidents often occur in an instant, which makes it necessary to obtain traffic data with high resolution. High-resolution micro traffic data (HRMTD) indicates that the spatial resolution reaches the centimeter level and that the temporal resolution reaches the millisecond level. The position, direction, speed, and acceleration of objects on the road can be extracted with HRMTD. In this paper, a LiDAR sensor was installed at the roadside for data collection. An adjacent-frame fusion method for vehicle detection and tracking in complex traffic circumstances is presented. Compared with the previous research, objects can be detected and tracked without object model extraction or a bounding box description. In addition, problems caused by occlusion can be improved using adjacent frames fusion in the vehicle detection and tracking algorithms in this paper. The data processing procedure are as follows: selection of area of interest, ground point removal, vehicle clustering, and vehicle tracking. The algorithm has been tested at different sites (in Reno and Suzhou), and the results demonstrate that the algorithm can perform well in both simple and complex application scenarios.
    Materialart: Online-Ressource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198119844457
    Sprache: Englisch
    Verlag: SAGE Publications
    Publikationsdatum: 2019
    ZDB Id: 2403378-9
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Online-Ressource
  • 3
    Online-Ressource
    Online-Ressource
    Automatic Vehicle Classification using Roadside LiDAR Data
    SAGE Publications ; 2019
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2673, No. 6 ( 2019-06), p. 153-164
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2673, No. 6 ( 2019-06), p. 153-164
    Kurzfassung: This research presented a new approach for vehicle classification using roadside LiDAR sensor. Six features (one feature, object height profile, contains 10 sub-features) extracted from the vehicle trajectories were applied to distinguish different classes of vehicles. The vehicle classification aims to assign the objects into ten different types defined by FHWA. A database containing 1,056 manually marked samples and their corresponding pictures was provided for analysis. Those samples were collected at different scenarios (roads and intersections, different speed limits, day and night, different distance to LiDAR, etc.). Naïve Bayes, K-nearest neighbor classification, random forest (RF), and support vector machine were applied for vehicle classification. The results showed that the performance of different methods varied by class. RF has the highest overall accuracy among those investigated methods. Some types were merged together to serve different types of users, which can also improve the accuracy of vehicle classification. The validation indicated that the distance between the object and the roadside LiDAR can influence the accuracy. This research also provided the distribution of the overall accuracy of RF along the distance to LiDAR. For the VLP-16 LiDAR, to achieve an accuracy of 91.98%, the distance between the object and LiDAR should be less than 30 ft. Users can set up the location of the roadside LiDAR based on their own requirements of the classification accuracy.
    Materialart: Online-Ressource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198119843857
    Sprache: Englisch
    Verlag: SAGE Publications
    Publikationsdatum: 2019
    ZDB Id: 2403378-9
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Online-Ressource
  • 4
    Online-Ressource
    Online-Ressource
    Automatic Background Filtering Method for Roadside LiDAR Data
    SAGE Publications ; 2018
    In:  Transportation Research Record: Journal of the Transportation Research Board Vol. 2672, No. 45 ( 2018-12), p. 106-114
    Details
    In: Transportation Research Record: Journal of the Transportation Research Board, SAGE Publications, Vol. 2672, No. 45 ( 2018-12), p. 106-114
    Kurzfassung: The high-resolution micro traffic data (HRMTD) of all roadway users is important for serving the connected-vehicle system in mixed traffic situations. The roadside LiDAR sensor gives a solution to providing HRMTD from real-time 3D point clouds of its scanned objects. Background filtering is the preprocessing step to obtain the HRMTD of different roadway users from roadside LiDAR data. It can significantly reduce the data processing time and improve the vehicle/pedestrian identification accuracy. An algorithm is proposed in this paper, based on the spatial distribution of laser points, which filters both static and moving background efficiently. Various thresholds of point density are applied in this algorithm to exclude background at different distances from the roadside sensor. The case study shows that the algorithm can filter background LiDAR points in different situations (different road geometries, different traffic demands, day/night time, different speed limits). Vehicle and pedestrian shape can be retained well after background filtering. The low computational load guarantees this method can be applied for real-time data processing such as vehicle monitoring and pedestrian tracking.
    Materialart: Online-Ressource
    ISSN: 0361-1981 , 2169-4052
    URL: Article
    DOI: 10.1177/0361198118775841
    Sprache: Englisch
    Verlag: SAGE Publications
    Publikationsdatum: 2018
    ZDB Id: 2403378-9
    Standort Signatur Einschränkungen Verfügbarkeit
    BibTip Andere fanden auch interessant ...
    Online-Ressource
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